Using Machine Learning and Business Intelligence Combining for Building Reliable Demand Forecasting Models

Aug 2, 2026

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Machine learning combined with business intelligence supports robust demand forecasting by extracting complex patterns from historical data and converting them into actionable insights. The research examines how BI tools and ML models improve forecasting accuracy by assessing influences such as sales, marketing spend, promotions, price, and customer traffic across 2014–2023. Using secondary industry reports, strong correlations are observed between sales, customer traffic, and marketing spend. Results indicate that joint BI and ML improve decision-making, enabling more reliable prediction for inventory, planning, and business outcomes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Demand Forecasting

Using Business Intelligence in Demand Forecasting

Machine Learning (ML) for Demand Forecasting

Research Objectives

Literature Review

Using Machine Learning and Business Intelligence Combining for Building Reliable Demand Forecasting Models Dr Shweta Paradkar Assistant Professor, Jesus and Mary College Abstract Machine learning and business intelligence in combination make for very robust demand forecasting models. Based on historical data, machine learning identifies complex patterns, and with business intelligence, it helps transform this data into meaningful insights. This allows for making accurate predictions, optimizing inventories, improving planning, and making better decisions. The research is conducted to explore the usage of business intelligence (BI) tools and machine learning models to enhance the accuracy of demand forecasting, by analyzing the impact of sales, marketing spends, promotions, price, and customer traffic from 2014 to 2023. The research was done using secondary data sourced from industry reports, where the results showed strong correlations between sales, customer traffic, and marketing spend. This clearly indicates that using BI tools together with machine learning has better implications for decision-making, thus giving accurate forecasting models that help the business. Keywords: Machine Learning, Business Intelligence, Demand Forecasting,Predictive Analysis 1. INTRODUCTION Machine learning with business intelligence is, lately, emerging as an integrated component with which many firms enhance accuracy in demand forecasting. Indeed, demand forecasting, wherein a company foretells or predicts customer demands in advance by basing the data on the actual past behaviors, has increasingly become key in inventory management, proper supply chain handling, and strategic business choices. The simple reason why there are imperfections with old-fashioned forecast techniques is their inadequacy to recognize the real complexities embedded inmost cases. Organization now has a better opportunity in order to construct more robust as well as flexible forecasting models by using the advanced features of ML with BI analytical insights. 1.1. Demand Forecasting This area of predictive analytics best explains how consumers are demanding things, namely, goods and services. As such, it makes projections based on historical data analyzed as well as the prevailing present conditions within a given market; it thereby makes an estimated demand in the future and aligns with it in terms of preparedness by means of requirement on the side of supplies. Demand forecasting, even though not a science in the exact meaning of the word, still plays an essential role in production planning and supply chain management. Demand forecasting results in strategic and long-term decision making in every way from budgeting and financial planning to capacity planning, sales and marketing planning, and capital expenditure. 1.2. Using Business Intelligence in Demand Forecasting With major breakthroughs in artificial intelligence and machine learning, businesses are also investing in advanced analytics as a way of getting ahead in the game and raising the bottom line. One such area is called predictive analytics, where companies derive information from existing data on how to buy patterns and foretell future trends. By combining data, statistical algorithms, and machine learning, predictive analytics determines the possible future outcomes from past situations. This technology is implemented in every industry, starting from banking to retail, so as to predict customer responses or purchases, forecast inventory, and manage resources or even check for fraud. Predictive analytics has been around for decades, but it is increasingly going mainstream, especially with more significant volumes of data and easier-to-access software just ready to be transformed Machine Learning (ML) for Demand Forecasting Machine learning offers an innovative approach to demand forecasting by breaking through the large and complex datasets; it identifies non-linear rel

FAQs

How does combining machine learning with business intelligence improve demand forecasting?

Machine learning detects complex patterns in historical data, while business intelligence transforms that data into meaningful insights for more accurate predictions and better planning decisions.

Which factors were analyzed to enhance demand forecasting accuracy?

The study evaluates impacts of sales, marketing spends, promotions, price, and customer traffic over the period from 2014 to 2023.

What were the key findings from the secondary data analysis?

The results showed strong correlations between sales, customer traffic, and marketing spend, indicating that BI tools together with machine learning support improved decision-making and forecasting reliability.

Aug 2, 2026

Upload by Sage